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Refactoring de TF_dict

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Harle, Antoine (Contracteur) 2019-11-14 21:17:54 -05:00
parent fd4dcdb392
commit 103277fadd
8 changed files with 245 additions and 23 deletions
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61
higher/transformations.py
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@ -3,30 +3,54 @@ import kornia
import random
### Available TF for Dataug ###
'''
TF_dict={ #f(mag_normalise)=mag_reelle
## Geometric TF ##
'Identity' : (lambda mag: None),
'FlipUD' : (lambda mag: None),
'FlipLR' : (lambda mag: None),
'Rotate': (lambda mag: random.randint(-int_parameter(mag, maxval=30), int_parameter(mag, maxval=30))),
'TranslateX': (lambda mag: [random.randint(-int_parameter(mag, maxval=20), int_parameter(mag, maxval=20)), 0]),
'TranslateY': (lambda mag: [0, random.randint(-int_parameter(mag, maxval=20), int_parameter(mag, maxval=20))]),
'ShearX': (lambda mag: [random.uniform(-float_parameter(mag, maxval=0.3), float_parameter(mag, maxval=0.3)), 0]),
'ShearY': (lambda mag: [0, random.uniform(-float_parameter(mag, maxval=0.3), float_parameter(mag, maxval=0.3))]),
'Rotate': (lambda mag: rand_int(mag,maxval=30)),
'TranslateX': (lambda mag: [rand_int(mag,maxval=20), 0]),
'TranslateY': (lambda mag: [0, rand_int(mag,maxval=20)]),
'ShearX': (lambda mag: [rand_float(mag, maxval=0.3), 0]),
'ShearY': (lambda mag: [0, rand_float(mag, maxval=0.3)]),
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast': (lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
'Color':(lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
'Brightness':(lambda mag: random.uniform(1., float_parameter(mag, maxval=1.9))),
'Sharpness':(lambda mag: random.uniform(0.1, float_parameter(mag, maxval=1.9))),
'Posterize': (lambda mag: random.randint(4, int_parameter(mag, maxval=8))),
'Solarize': (lambda mag: random.randint(1, int_parameter(mag, maxval=256))/256.), #=>Image entre [0,1] #Pas opti pour des batch
'Contrast': (lambda mag: rand_float(mag,minval=0.1, maxval=1.9)),
'Color':(lambda mag: rand_float(mag,minval=0.1, maxval=1.9)),
'Brightness':(lambda mag: rand_float(mag,minval=1., maxval=1.9)),
'Sharpness':(lambda mag: rand_float(mag,minval=0.1, maxval=1.9)),
'Posterize': (lambda mag: rand_int(mag,minval=4, maxval=8)),
'Solarize': (lambda mag: rand_int(mag,minval=1, maxval=256)/256.), #=>Image entre [0,1] #Pas opti pour des batch
#Non fonctionnel
#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
#'Equalize': (lambda mag: None),
}
'''
TF_dict={
## Geometric TF ##
'Identity' : (lambda x, mag: x),
'FlipUD' : (lambda x, mag: flipUD(x)),
'FlipLR' : (lambda x, mag: flipLR(x)),
'Rotate': (lambda x, mag: rotate(x, angle=torch.tensor([rand_int(mag, maxval=30)for _ in x], device=x.device))),
'TranslateX': (lambda x, mag: translate(x, translation=torch.tensor([[rand_int(mag, maxval=20), 0] for _ in x], device=x.device))),
'TranslateY': (lambda x, mag: translate(x, translation=torch.tensor([[0, rand_int(mag, maxval=20)] for _ in x], device=x.device))),
'ShearX': (lambda x, mag: shear(x, shear=torch.tensor([[rand_float(mag, maxval=0.3), 0] for _ in x], device=x.device))),
'ShearY': (lambda x, mag: shear(x, shear=torch.tensor([[0, rand_float(mag, maxval=0.3)] for _ in x], device=x.device))),
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast': (lambda x, mag: contrast(x, contrast_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
'Color':(lambda x, mag: color(x, color_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
'Brightness':(lambda x, mag: brightness(x, brightness_factor=torch.tensor([rand_float(mag, minval=1., maxval=1.9) for _ in x], device=x.device))),
'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=torch.tensor([rand_float(mag, minval=0.1, maxval=1.9) for _ in x], device=x.device))),
'Posterize': (lambda x, mag: posterize(x, bits=torch.tensor([rand_int(mag, minval=4, maxval=8) for _ in x], device=x.device))),
'Solarize': (lambda x, mag: solarize(x, thresholds=torch.tensor([rand_int(mag,minval=1, maxval=256)/256. for _ in x], device=x.device))) , #=>Image entre [0,1] #Pas opti pour des batch
#Non fonctionnel
#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
#'Equalize': (lambda mag: None),
}
def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/256 = 0.0039)
return (float_image*255.).type(torch.uint8)
@ -34,8 +58,19 @@ def int_image(float_image): #ATTENTION : legere perte d'info (granularite : 1/25
def float_image(int_image):
return int_image.type(torch.float)/255.
def rand_inverse(value):
return value if random.random() < 0.5 else -value
#def rand_inverse(value):
# return value if random.random() < 0.5 else -value
def rand_int(mag, maxval, minval=None): #[(-maxval,minval), maxval]
real_max = int_parameter(mag, maxval=maxval)
if not minval : minval = -real_max
return random.randint(minval, real_max)
def rand_float(mag, maxval, minval=None): #[(-maxval,minval), maxval]
real_max = float_parameter(mag, maxval=maxval)
if not minval : minval = -real_max
return random.uniform(minval, real_max)
#https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py#L137
PARAMETER_MAX = 10 # What is the max 'level' a transform could be predicted